github_url <- "https://github.com/BU-IE-582/fall-23-burakcetiner3/raw/main/all_ticks_wide.xlsx"
local_file_path <- "all_ticks_wide.xlsx"
download.file(url = github_url, destfile = local_file_path, mode = "wb")
Dataset <- read_excel(local_file_path)
summary(Dataset)
## timestamp AEFES AKBNK AKSA
## Length:50012 Min. : 0.0001 Min. :0.0001 Min. : 0.0001
## Class :character 1st Qu.:19.1605 1st Qu.:5.8500 1st Qu.: 5.2088
## Mode :character Median :20.6465 Median :6.3057 Median : 6.9853
## Mean :20.9822 Mean :6.4731 Mean : 7.1275
## 3rd Qu.:22.7320 3rd Qu.:6.9325 3rd Qu.: 8.7200
## Max. :28.5090 Max. :9.2124 Max. :15.1189
## NA's :1881 NA's :803 NA's :1418
## AKSEN ALARK ALBRK ANACM
## Min. :0.000 Min. :0.0001 Min. :1.026 Min. :0.0001
## 1st Qu.:2.670 1st Qu.:1.5689 1st Qu.:1.225 1st Qu.:1.0470
## Median :2.930 Median :1.9376 Median :1.360 Median :1.2597
## Mean :3.183 Mean :2.0609 Mean :1.365 Mean :1.6721
## 3rd Qu.:3.750 3rd Qu.:2.4214 3rd Qu.:1.500 3rd Qu.:2.4021
## Max. :5.190 Max. :3.5143 Max. :2.190 Max. :3.5021
## NA's :1841 NA's :1677 NA's :3150 NA's :1847
## ARCLK ASELS ASUZU AYGAZ
## Min. : 0.0001 Min. : 0.0001 Min. : 0.0001 Min. : 0.0001
## 1st Qu.:11.7111 1st Qu.: 4.9403 1st Qu.: 5.0748 1st Qu.: 5.9515
## Median :15.0100 Median : 9.2757 Median : 5.9496 Median : 7.7238
## Mean :15.3881 Mean :13.4325 Mean : 6.4670 Mean : 8.1019
## 3rd Qu.:19.0877 3rd Qu.:22.7567 3rd Qu.: 7.1200 3rd Qu.:10.2690
## Max. :26.4278 Max. :46.7616 Max. :15.2800 Max. :13.5935
## NA's :967 NA's :1209 NA's :1579 NA's :1893
## BAGFS BANVT BRISA CCOLA
## Min. : 0.0001 Min. : 0.000 Min. : 0.0001 Min. : 0.0001
## 1st Qu.: 8.2618 1st Qu.: 2.590 1st Qu.: 5.8900 1st Qu.:31.9782
## Median :10.6100 Median : 3.710 Median : 6.7300 Median :34.8215
## Mean :10.4071 Mean : 7.628 Mean : 6.5449 Mean :36.8907
## 3rd Qu.:12.3500 3rd Qu.:11.930 3rd Qu.: 7.3300 3rd Qu.:42.0497
## Max. :38.4352 Max. :28.680 Max. :10.3275 Max. :54.2208
## NA's :1362 NA's :2061 NA's :1075 NA's :1263
## CEMAS ECILC EREGL FROTO
## Min. :0.000 Min. :0.0001 Min. : 0.0001 Min. : 0.0001
## 1st Qu.:0.700 1st Qu.:1.1723 1st Qu.: 2.1812 1st Qu.:21.4938
## Median :0.870 Median :1.8214 Median : 3.0360 Median :27.1182
## Mean :1.209 Mean :2.0759 Mean : 4.1795 Mean :32.7637
## 3rd Qu.:1.500 3rd Qu.:2.7809 3rd Qu.: 6.7587 3rd Qu.:48.5116
## Max. :7.010 Max. :4.2278 Max. :10.4710 Max. :65.4192
## NA's :3618 NA's :1520 NA's :839 NA's :1017
## GARAN GOODY GUBRF HALKB
## Min. : 0.0001 Min. : 0.0001 Min. : 0.0001 Min. : 0.0001
## 1st Qu.: 7.0154 1st Qu.: 2.4277 1st Qu.: 3.2765 1st Qu.: 8.7205
## Median : 7.6542 Median : 3.1920 Median : 4.2500 Median :10.6531
## Mean : 7.8997 Mean : 3.1025 Mean : 4.3283 Mean :10.9194
## 3rd Qu.: 8.6786 3rd Qu.: 3.5966 3rd Qu.: 5.1300 3rd Qu.:13.4909
## Max. :12.1554 Max. :58.7574 Max. :13.6191 Max. :20.2365
## NA's :704 NA's :1051 NA's :955 NA's :941
## ICBCT ISCTR ISDMR ISFIN
## Min. : 0.000 Min. :0.0001 Mode:logical Min. :0.000
## 1st Qu.: 1.560 1st Qu.:4.3200 TRUE:12227 1st Qu.:0.564
## Median : 2.030 Median :4.8543 NA's:37785 Median :0.864
## Mean : 2.829 Mean :5.1266 Mean :1.559
## 3rd Qu.: 4.070 3rd Qu.:5.8203 3rd Qu.:1.674
## Max. :11.270 Max. :7.9639 Max. :9.830
## NA's :5676 NA's :791 NA's :7135
## ISYAT KAREL KARSN KCHOL
## Min. :0.000 Min. :0.000 Min. :0.0001 Min. : 0.0001
## 1st Qu.:0.441 1st Qu.:1.531 1st Qu.:1.1100 1st Qu.: 9.7368
## Median :0.496 Median :1.820 Median :1.2874 Median :12.0449
## Mean :0.537 Mean :3.178 Mean :1.3269 Mean :12.2483
## 3rd Qu.:0.633 3rd Qu.:5.250 3rd Qu.:1.4700 3rd Qu.:15.1693
## Max. :1.150 Max. :9.460 Max. :2.5000 Max. :19.1500
## NA's :6828 NA's :3980 NA's :1485 NA's :919
## KRDMB KRDMD MGROS OTKAR
## Min. :0.0001 Min. :0.0001 Min. : 0.0001 Min. : 0.0001
## 1st Qu.:1.5612 1st Qu.:1.0845 1st Qu.:16.6600 1st Qu.: 56.7757
## Median :2.2007 Median :1.3979 Median :19.1100 Median : 82.8224
## Mean :2.2228 Mean :1.7684 Mean :19.5764 Mean : 81.4195
## 3rd Qu.:2.7273 3rd Qu.:2.1690 3rd Qu.:22.1000 3rd Qu.:105.4988
## Max. :4.4960 Max. :4.9510 Max. :30.2600 Max. :139.4288
## NA's :2480 NA's :851 NA's :1109 NA's :1227
## PARSN PETKM PGSUS PRKME
## Min. : 0.000 Min. :0.0001 Mode :logical Min. :0.0001
## 1st Qu.: 4.570 1st Qu.:1.2869 FALSE:1 1st Qu.:2.3895
## Median : 7.890 Median :2.2845 TRUE :45220 Median :2.7400
## Mean : 8.277 Mean :2.5392 NA's :4791 Mean :2.9271
## 3rd Qu.:10.650 3rd Qu.:3.8828 3rd Qu.:3.4365
## Max. :29.820 Max. :5.7697 Max. :5.4300
## NA's :4687 NA's :828 NA's :1546
## SAHOL SASA SISE SKBNK
## Min. : 0.0001 Min. :0.0001 Min. :0.0001 Min. :0.0001
## 1st Qu.: 7.9652 1st Qu.:0.3192 1st Qu.:1.9220 1st Qu.:1.2000
## Median : 8.6079 Median :0.7335 Median :2.6682 Median :1.5100
## Mean : 8.6159 Mean :2.2949 Mean :3.0484 Mean :1.4737
## 3rd Qu.: 9.2682 3rd Qu.:4.9473 3rd Qu.:4.1460 3rd Qu.:1.7207
## Max. :11.6826 Max. :8.4260 Max. :6.9230 Max. :2.2516
## NA's :917 NA's :2379 NA's :922 NA's :2742
## SODA TCELL THYAO TKFEN
## Min. :0.0001 Min. : 0.0001 Min. : 0.0001 Min. : 0.0001
## 1st Qu.:1.4758 1st Qu.: 8.5663 1st Qu.: 6.4300 1st Qu.: 4.3190
## Median :2.6684 Median : 9.7001 Median : 7.7800 Median : 5.7532
## Mean :3.1896 Mean : 9.8280 Mean : 9.2888 Mean : 9.1918
## 3rd Qu.:4.2861 3rd Qu.:11.2364 3rd Qu.:12.2700 3rd Qu.:14.2468
## Max. :7.7659 Max. :15.8125 Max. :19.9500 Max. :27.3200
## NA's :1736 NA's :869 NA's :730 NA's :1082
## TOASO TRKCM TSKB TTKOM
## Min. : 0.0001 Min. :0.0001 Min. :0.0001 Min. :0.0001
## 1st Qu.:10.3656 1st Qu.:1.1742 1st Qu.:0.8254 1st Qu.:5.2673
## Median :16.5554 Median :1.6270 Median :0.9373 Median :5.7464
## Mean :16.5973 Mean :2.0278 Mean :0.9452 Mean :5.6607
## 3rd Qu.:20.6513 3rd Qu.:2.9826 3rd Qu.:1.0244 3rd Qu.:6.2600
## Max. :29.9218 Max. :4.6432 Max. :1.4208 Max. :7.3500
## NA's :1066 NA's :1126 NA's :1628 NA's :935
## TUKAS TUPRS USAK VAKBN
## Min. :0.650 Min. : 0.0001 Min. :0.0001 Min. :0.0001
## 1st Qu.:1.060 1st Qu.: 34.5491 1st Qu.:0.9571 1st Qu.:4.0322
## Median :1.530 Median : 49.5542 Median :1.0500 Median :4.4742
## Mean :1.738 Mean : 62.9945 Mean :1.2205 Mean :4.7354
## 3rd Qu.:2.130 3rd Qu.: 93.4287 3rd Qu.:1.3708 3rd Qu.:5.2460
## Max. :5.920 Max. :139.2937 Max. :2.7578 Max. :7.5814
## NA's :4083 NA's :869 NA's :2353 NA's :800
## VESTL YATAS YKBNK YUNSA
## Min. : 0.000 Min. : 0.000 Min. :0.0001 Min. :0.000
## 1st Qu.: 4.020 1st Qu.: 0.389 1st Qu.:2.2682 1st Qu.:3.007
## Median : 6.320 Median : 0.966 Median :2.6093 Median :4.108
## Mean : 5.943 Mean : 2.434 Mean :2.5663 Mean :4.080
## 3rd Qu.: 7.450 3rd Qu.: 4.230 3rd Qu.:2.8740 3rd Qu.:4.721
## Max. :14.540 Max. :10.675 Max. :3.9581 Max. :9.528
## NA's :1231 NA's :3957 NA's :787 NA's :4484
## ZOREN
## Min. :0.0001
## 1st Qu.:1.0338
## Median :1.2500
## Mean :1.2481
## 3rd Qu.:1.4265
## Max. :2.4430
## NA's :1205
First I wanted to see the Five Number Summary Statistics (summary) of each dataset. I realized that there are many missing values (NAs) and I decide to get rid off them
Dataset1 = Dataset[complete.cases(Dataset),] #removing NAs
Then instead of dealing with 61 different stocks, I decide to choose 10 of them. I chose the foloowing 10 stocks because I have an insight about them due to my brother’s trades and advices on Turkish financial market.
AEFES = Dataset1$AEFES
AKBNK = Dataset1$AKBNK
AKSA = Dataset1$AKSA
AKSEN = Dataset1$AKSEN
SASA = Dataset1$SASA
CCOLA = Dataset1$CCOLA
GARAN = Dataset1$GARAN
MGROS = Dataset1$MGROS
TCELL = Dataset1$TCELL
TUPRS = Dataset1$TUPRS
First I will combine them to see the correlation relationships
Table = cbind(AKBNK,AKSA,AKSEN,SASA,CCOLA,GARAN,MGROS,TCELL, TUPRS) # I construct a new table to see the pairs
pairs(Table) # scatter diagrams of each pairs
It seems like there is positive strong correlation between AKBNK and GARAN. Let’s investigate further
plot(AKBNK, GARAN)
round(cor(AKBNK, GARAN),3) # r = 0.907 indicates a strong positive correlation between AKBNK and GARAN.
## [1] 0.907
summary(AKBNK)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.607 5.851 6.323 6.433 6.820 9.212
summary(GARAN)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.640 7.590 8.220 8.272 8.990 12.098
boxplot(AKBNK) # This function visualize 5-number-summary statistics of AKBNK stock
boxplot(GARAN) # This function visualize 5-number-summary statistics of GARAN stock
To finalize descriptive statistics of each stock we have to define standard deviation, variance, mode, interquartile range, and finally the histogram of each stock.
mode_AKBNK = sort(table(AKBNK), decreasing=T)
mode_AKBNK[1] #mode of AKBNK stock is 6.79
## 6.79
## 52
sd(AKBNK) #standard deviation of AKBNK
## [1] 0.8210988
var(AKBNK) #variance of AKBNK
## [1] 0.6742033
IQR(AKBNK) #interquartile range of AKBNK
## [1] 0.9691
hist(AKBNK, main="The histogram of stock AKBNK", col="skyblue")
mode_GARAN = sort(table(GARAN), decreasing=T)
mode_GARAN[1] #mode of GARAN stock is 8.95
## 8.95
## 62
sd(GARAN) #standard deviation of GARAN
## [1] 1.074116
var(GARAN) #variance of GARAN
## [1] 1.153726
IQR(GARAN) #interquartile range of GARAN
## [1] 1.4
hist(GARAN,main="The histogram of stock GARAN", col="skyblue")
2nd finding: It seems like there is positive correlation between AKSA and AKSEN. Let’s investigate further
summary(AKSA)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.367 7.941 8.520 8.813 9.685 15.110
summary(AKSEN)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.040 2.518 3.020 3.231 3.990 5.190
boxplot(AKSA) # This function visualize 5-number-summary statistics of AKSA stock
boxplot(AKSEN) # This function visualize 5-number-summary statistics of AKSEN stock
plot(AKSA, AKSEN)
round(cor(AKSA, AKSEN),3) # 0.636 indicates a positive correlation between AKSA and AKSEN.
## [1] 0.636
To finalize descriptive statistics of each stock we have to define standard deviation, variance, mode, interquartile range, and finally the histogram of each stock.
mode_AKSA = sort(table(AKSA), decreasing=T)
mode_AKSA[1] #mode of AKSA stock is 8.0397
## 8.0397
## 50
sd(AKSA) #standard deviation of AKSA
## [1] 1.575767
var(AKSA) #variance of AKSA
## [1] 2.48304
IQR(AKSA) #interquartile range of AKSA
## [1] 1.743575
hist(AKSA, main="The histogram of stock AKSA", col="skyblue")
mode_AKSEN = sort(table(AKSEN), decreasing=T)
mode_AKSEN[1] #mode of AKSEN stock is 2.35
## 2.35
## 158
sd(AKSEN) #standard deviation of AKSEN
## [1] 0.7504789
var(AKSEN) #variance of AKSEN
## [1] 0.5632186
IQR(AKSEN) #interquartile range of AKSEN
## [1] 1.4725
hist(AKSEN, main="The histogram of stock AKSEN", col="skyblue")
AKBNK_april = AKBNK[11:58]; AKBNK_april
## [1] 6.4277 6.3155 6.3075 6.3475 6.4677 6.4036 6.3877 6.4117 6.2914 6.3556
## [11] 6.2674 6.2914 6.2995 6.3716 6.3796 6.4117 6.4438 6.5318 6.5078 6.5158
## [21] 6.4838 6.5078 6.5720 6.7643 6.7483 6.7803 6.7883 6.7964 6.9566 6.9807
## [31] 7.0127 6.9646 6.8204 6.8204 6.8926 6.8445 6.8364 6.8204 6.7803 6.6921
## [41] 6.6761 6.8685 6.8926 6.8605 6.8765 6.9086 6.9325 6.8926
AKBNK_may = AKBNK[59:101]
AKBNK_june = AKBNK[102:150]
AKBNK_july = AKBNK[151:182]
AKBNK_august = AKBNK[183:224]
AKBNK_september = AKBNK[225:269]
GARAN_april = GARAN[11:58]; GARAN_april
## [1] 7.5045 7.3764 7.3764 7.4039 7.6599 7.5776 7.5776 7.5959 7.4314 7.5045
## [11] 7.3764 7.3582 7.3764 7.4862 7.4954 7.5136 7.5684 7.5959 7.5868 7.5410
## [21] 7.4497 7.4679 7.5228 7.6416 7.6142 7.6233 7.6325 7.7787 7.9798 7.9889
## [31] 8.0255 8.0529 7.9707 7.9889 8.0529 7.9066 7.9066 7.7329 7.7378 7.7192
## [41] 7.7378 7.9050 7.9515 7.9050 7.9329 8.0072 8.0444 8.0072
GARAN_may = GARAN[59:101]
GARAN_june = GARAN[102:150]
GARAN_july = GARAN[151:182]
GARAN_august = GARAN[183:224]
GARAN_september = GARAN[225:269]
plot(GARAN_april, AKBNK_april)
cor(GARAN_april, AKBNK_april)
## [1] 0.9288523
plot(GARAN_may, AKBNK_may)
cor(GARAN_may, AKBNK_may)
## [1] 0.819365
plot(GARAN_june, AKBNK_june)
cor(GARAN_june, AKBNK_june)
## [1] 0.7575476
plot(GARAN_july, AKBNK_july)
cor(GARAN_july, AKBNK_july)
## [1] 0.9934856
plot(GARAN_august, AKBNK_august)
cor(GARAN_august, AKBNK_august)
## [1] 0.9493835
plot(GARAN_september, AKBNK_september)
cor(GARAN_september, AKBNK_september)
## [1] 0.925077
AKBNK_2016 = AKBNK[1:376]; AKBNK_2016
## [1] 6.2904 6.2510 6.2510 6.2904 6.2432 6.2353 6.3475 6.4117 6.4036 6.4117
## [11] 6.4277 6.3155 6.3075 6.3475 6.4677 6.4036 6.3877 6.4117 6.2914 6.3556
## [21] 6.2674 6.2914 6.2995 6.3716 6.3796 6.4117 6.4438 6.5318 6.5078 6.5158
## [31] 6.4838 6.5078 6.5720 6.7643 6.7483 6.7803 6.7883 6.7964 6.9566 6.9807
## [41] 7.0127 6.9646 6.8204 6.8204 6.8926 6.8445 6.8364 6.8204 6.7803 6.6921
## [51] 6.6761 6.8685 6.8926 6.8605 6.8765 6.9086 6.9325 6.8926 6.8525 6.7483
## [61] 6.4677 6.4597 6.4357 6.2434 6.0751 6.1231 6.1391 6.1151 6.2033 6.2273
## [71] 6.2113 6.2273 6.1391 6.1471 6.1952 6.1873 6.1712 6.1471 6.1712 6.1632
## [81] 6.2033 6.0110 6.0751 6.0991 6.0590 6.1552 6.1792 6.1873 6.0110 6.1632
## [91] 6.4518 6.4757 6.3636 6.3556 6.3075 6.3316 6.3716 6.4117 6.4117 6.4838
## [101] 6.3877 6.3475 6.3316 6.3556 6.2514 6.2674 6.4117 6.4117 6.4197 6.4518
## [111] 6.4677 6.5078 6.3395 6.3235 6.3475 6.4518 6.4277 6.3636 6.3475 6.3155
## [121] 6.2834 6.2914 6.2514 6.2594 6.2754 6.2353 6.2594 6.2434 6.3075 6.2995
## [131] 6.2914 6.2674 6.1712 6.1632 6.2273 6.3796 6.4757 6.5720 6.5720 6.6040
## [141] 6.5960 6.5799 6.4036 6.5399 6.5478 6.5559 6.4998 6.5799 6.5799 6.5799
## [151] 6.6761 6.6681 6.7563 6.7242 6.7563 7.0849 7.0768 7.0689 7.0849 7.0768
## [161] 7.1410 7.0207 6.5960 6.5880 6.4597 6.3877 6.3556 6.1792 6.0910 5.9469
## [171] 6.0590 5.9469 5.9628 6.0670 6.1312 6.1312 6.1712 6.0349 6.1792 6.0910
## [181] 6.0910 6.0590 6.2273 6.2594 6.1391 6.1312 6.1312 6.0349 6.0030 6.2193
## [191] 6.1792 6.3316 6.4277 6.4438 6.4917 6.4518 6.5399 6.5960 6.5078 6.5078
## [201] 6.5559 6.5559 6.4036 6.4197 6.5559 6.5158 6.5158 6.5238 6.5639 6.5720
## [211] 6.4998 6.5158 6.5478 6.4357 6.4357 6.2273 6.2353 6.2754 6.4036 6.4036
## [221] 6.3475 6.2754 6.2914 6.2834 6.3235 6.3235 6.3316 6.4277 6.4917 6.5639
## [231] 6.5880 6.5960 6.5880 6.5960 6.5238 6.5720 6.5720 6.6040 6.5639 6.5559
## [241] 6.4518 6.4998 6.4597 6.4357 6.4917 6.6360 6.5880 6.5720 6.5960 6.6281
## [251] 6.7163 6.7563 6.7483 6.7483 6.7403 6.4036 6.3636 6.5158 6.5158 6.5318
## [261] 6.6441 6.6601 6.6441 6.5318 6.5158 6.4597 6.4357 6.4438 6.4838 6.5399
## [271] 6.5399 6.4677 6.4117 6.5238 6.4917 6.4917 6.5478 6.5078 6.4998 6.4917
## [281] 6.5158 6.5318 6.5078 6.3475 6.4036 6.6601 6.6842 6.7563 6.7483 6.7082
## [291] 6.7082 6.7483 6.7643 6.7563 6.7563 6.6601 6.6521 6.6601 6.6761 6.6761
## [301] 6.6601 6.5639 6.5639 6.5720 6.5720 6.6120 6.6360 6.6681 6.5238 6.5318
## [311] 6.4917 6.5158 6.3877 6.2834 6.1712 6.1712 6.2674 6.2754 6.3155 6.2033
## [321] 6.3316 6.4117 6.3716 6.1952 6.3235 6.2273 6.2353 6.2674 6.3235 6.3796
## [331] 6.4036 6.2353 6.0910 6.1552 6.2033 6.1792 6.1312 5.9789 5.9789 5.9469
## [341] 5.9228 6.0910 6.0751 6.1632 6.1231 6.0751 6.0751 6.1151 6.2514 6.2594
## [351] 6.2434 6.3075 6.3155 6.2514 6.3075 6.3716 6.3796 6.3877 6.3075 6.2914
## [361] 6.2434 6.2754 6.2914 6.3155 6.2754 6.2434 6.2353 6.1873 6.2033 6.1471
## [371] 6.2434 6.2434 6.2273 6.2273 6.2674 6.2434
AKBNK_2017 = AKBNK[377:1055]
AKBNK_2018 = AKBNK[1056:5318]
AKBNK_2019 = AKBNK[5319:9228]
GARAN_2016 = GARAN[1:376]; GARAN_2016
## [1] 7.2669 7.2577 7.2943 7.3126 7.2302 7.2211 7.3126 7.4131 7.4222 7.5136
## [11] 7.5045 7.3764 7.3764 7.4039 7.6599 7.5776 7.5776 7.5959 7.4314 7.5045
## [21] 7.3764 7.3582 7.3764 7.4862 7.4954 7.5136 7.5684 7.5959 7.5868 7.5410
## [31] 7.4497 7.4679 7.5228 7.6416 7.6142 7.6233 7.6325 7.7787 7.9798 7.9889
## [41] 8.0255 8.0529 7.9707 7.9889 8.0529 7.9066 7.9066 7.7329 7.7378 7.7192
## [51] 7.7378 7.9050 7.9515 7.9050 7.9329 8.0072 8.0444 8.0072 7.9700 7.9050
## [61] 7.5521 7.5614 7.5242 7.3663 7.1619 7.0597 7.0318 6.9297 6.9947 7.0133
## [71] 7.0226 7.0690 7.0133 6.9854 7.0411 6.9854 6.9761 6.9297 6.9575 6.9761
## [81] 7.0411 6.8182 6.8832 6.9389 6.8646 6.9111 6.9204 6.9389 6.7346 6.8182
## [91] 7.1619 7.2084 7.0411 7.0318 6.9482 7.0133 7.0040 7.0411 7.0318 7.0783
## [101] 7.0226 6.8925 6.8739 6.8925 6.8089 6.8368 7.0411 7.0411 7.0690 7.1155
## [111] 7.1155 7.1619 7.0226 7.0411 7.0690 7.1805 7.1526 7.0969 7.0690 7.0597
## [121] 6.9947 6.9947 6.9668 6.9389 6.9482 6.9297 6.9297 6.9297 7.0226 6.9761
## [131] 6.9854 6.9204 6.9111 6.8925 6.9668 6.9668 7.0876 7.1991 7.2084 7.2176
## [141] 7.1805 7.2269 6.9482 6.9854 6.9947 7.0133 6.9947 7.1805 7.1712 7.0504
## [151] 7.2641 7.2641 7.3663 7.4220 7.4499 7.6449 7.6542 7.7564 7.7657 7.7471
## [161] 7.8121 7.5985 7.1340 7.1340 7.0226 6.9668 6.9482 6.7717 6.7067 6.5210
## [171] 6.6510 6.5767 6.5674 6.7067 6.7624 6.7717 6.8275 6.6695 6.8089 6.7717
## [181] 6.7717 6.7531 6.8925 6.9668 6.8646 6.8646 6.8553 6.7810 6.7439 6.9854
## [191] 7.0690 7.2827 7.3105 7.3105 7.3384 7.1340 7.3291 7.3477 7.2548 7.2548
## [201] 7.3105 7.3663 7.2269 7.2548 7.3477 7.3105 7.3198 7.3291 7.3291 7.3384
## [211] 7.2548 7.2734 7.3013 7.2269 7.2176 7.0133 7.0411 7.1155 7.2362 7.2269
## [221] 7.1898 7.0411 7.0411 7.0876 7.1155 7.0876 7.0969 7.3570 7.4406 7.5149
## [231] 7.5521 7.5242 7.5242 7.5335 7.5335 7.5707 7.5985 7.6077 7.5892 7.5335
## [241] 7.5056 7.5892 7.5242 7.4963 7.5707 7.7192 7.6635 7.6449 7.6728 7.6635
## [251] 7.7935 7.8957 7.8864 7.8864 7.8028 7.4313 7.3477 7.4778 7.4778 7.5149
## [261] 7.5799 7.5985 7.6263 7.5149 7.4685 7.4034 7.4034 7.3849 7.4592 7.4871
## [271] 7.5149 7.4406 7.4034 7.4871 7.4778 7.4871 7.5521 7.4963 7.4685 7.4499
## [281] 7.4685 7.4963 7.4685 7.3013 7.4034 7.5521 7.5799 7.6635 7.6542 7.6263
## [291] 7.6356 7.6913 7.7564 7.7471 7.7657 7.7285 7.7285 7.7657 7.8214 7.8214
## [301] 7.8121 7.7099 7.7192 7.7006 7.7006 7.7471 7.8214 7.8586 7.6542 7.6728
## [311] 7.5799 7.5799 7.4871 7.3663 7.1155 7.1155 7.2548 7.2176 7.1991 7.0504
## [321] 7.2084 7.3013 7.2269 7.0504 7.1433 7.0597 7.0690 7.0783 7.1619 7.1062
## [331] 7.1155 6.9389 6.7810 6.8089 6.8646 6.8460 6.8275 6.6788 6.6881 6.8182
## [341] 6.8089 6.9761 6.9947 7.0226 6.9761 6.9575 6.9482 6.9482 7.0876 7.0969
## [351] 7.0969 7.0690 7.0597 7.0040 7.0876 7.1712 7.1805 7.1898 7.1433 7.1712
## [361] 7.0876 7.1155 7.1712 7.1991 7.2269 7.1898 7.1898 7.0690 7.0876 7.0133
## [371] 7.1062 7.0969 7.0690 7.0783 7.0783 7.0690
GARAN_2017 = GARAN[377:1055]
GARAN_2018 = GARAN[1056:5318]
GARAN_2019 = GARAN[5319:9228]
plot(GARAN_2016, AKBNK_2016)
cor(GARAN_2016, AKBNK_2016)
## [1] 0.8624163
plot(GARAN_2017, AKBNK_2017)
cor(GARAN_2017, AKBNK_2017)
## [1] 0.9453328
plot(GARAN_2018, AKBNK_2018)
cor(GARAN_2018, AKBNK_2018)
## [1] 0.9566644
plot(GARAN_2019, AKBNK_2019)
cor(GARAN_2019, AKBNK_2019)
## [1] 0.9311119
numerical_data = Dataset1[2:61]
data_normalized <- scale(numerical_data)
head(data_normalized)
## AEFES AKBNK AKSA AKSEN ALARK ALBRK ANACM
## [1,] -0.6713347 -0.1741357 -1.0239060 -0.8808708 -2.274540 0.4699192 -3.886826
## [2,] -0.7879552 -0.2221202 -0.9317604 -0.8941956 -2.265639 0.3781936 -3.910856
## [3,] -0.8365774 -0.2221202 -0.9317604 -0.9075205 -2.274540 0.4248166 -3.910856
## [4,] -0.8365774 -0.1741357 -0.9230027 -0.8941956 -2.238715 0.4248166 -3.898841
## [5,] -0.7296710 -0.2316196 -0.9273181 -0.9075205 -2.203113 0.4699192 -3.850553
## [6,] -0.6519066 -0.2412409 -0.9317604 -0.9075205 -2.203113 0.4699192 -3.850553
## ARCLK ASELS ASUZU AYGAZ BAGFS BANVT BRISA
## [1,] -0.04826430 -3.033768 -1.551689 -1.939165 3.848593 -2.951394 2.039219
## [2,] -0.02479650 -3.040067 -1.577119 -2.002972 3.772594 -2.969511 2.005148
## [3,] -0.05162206 -3.043228 -1.575443 -2.002972 3.752327 -2.975550 2.005148
## [4,] -0.04494304 -3.032700 -1.563595 -1.985643 3.797927 -2.984608 2.520925
## [5,] 0.04232216 -3.040067 -1.509323 -1.974002 3.797927 -3.002725 2.194713
## [6,] 0.05911095 -3.043228 -1.505913 -1.968136 3.797927 -3.005745 2.212836
## CCOLA CEMAS ECILC EREGL FROTO GARAN GOODY
## [1,] 1.266225 -1.034649 -2.547866 -3.361842 -2.841710 -0.9357146 -0.4832547
## [2,] 1.220707 -1.044689 -2.547866 -3.323912 -2.878351 -0.9442798 -0.4789407
## [3,] 1.207762 -1.034649 -2.559409 -3.319127 -2.892590 -0.9102052 -0.4703129
## [4,] 1.207762 -1.034649 -2.442207 -3.304912 -2.857997 -0.8931680 0.8683042
## [5,] 1.383221 -1.024609 -2.348755 -3.304912 -2.841710 -0.9698822 0.8683042
## [6,] 1.454734 -1.034649 -2.337213 -3.295481 -2.833574 -0.9783543 0.8683042
## GUBRF HALKB ICBCT ISCTR ISDMR ISFIN ISYAT KAREL
## [1,] 3.407026 1.502065 -1.656840 -1.203885 NaN -1.521447 -1.731656 -2.627192
## [2,] 3.353206 1.450525 -1.663890 -1.213421 NaN -1.517939 -1.689014 -2.627192
## [3,] 3.353206 1.439038 -1.667414 -1.213421 NaN -1.521447 -1.689014 -2.634389
## [4,] 3.407026 1.496412 -1.670939 -1.175383 NaN -1.517939 -1.689014 -2.627192
## [5,] 3.474437 1.439038 -1.656840 -1.175383 NaN -1.517939 -1.689014 -2.619995
## [6,] 3.447527 1.439038 -1.660365 -1.146881 NaN -1.517939 -1.731656 -2.619995
## KARSN KCHOL KRDMB KRDMD MGROS OTKAR PARSN
## [1,] -0.1806519 -1.417656 -1.152116 -1.498835 0.2732247 0.1726716 -1.514692
## [2,] -0.1394458 -1.345867 -1.221013 -1.539747 0.2342256 0.1270523 -1.505931
## [3,] -0.1806519 -1.373497 -1.221013 -1.549948 0.2316256 0.1270523 -1.503740
## [4,] -0.1394458 -1.340377 -1.221013 -1.549948 0.2472253 0.1371918 -1.501550
## [5,] -0.1394458 -1.378986 -1.255461 -1.549948 0.1614271 0.1118402 -1.453364
## [6,] -0.1806519 -1.362457 -1.255461 -1.560257 0.1614271 0.0966338 -1.455554
## PETKM PGSUS PRKME SAHOL SASA SISE SKBNK
## [1,] -3.260285 NaN -0.7423933 0.5464246 -3.623842 -2.824176 2.134641
## [2,] -3.270608 NaN -0.7590049 0.5372094 -3.627092 -2.841558 2.502474
## [3,] -3.280753 NaN -0.7590049 0.5277871 -3.628752 -2.850484 2.449927
## [4,] -3.250140 NaN -0.6089141 0.5839068 -3.625433 -2.850484 2.502474
## [5,] -3.117897 NaN -0.6923630 0.6678791 -3.623842 -2.789529 2.344832
## [6,] -3.117897 NaN -0.6923630 0.6959390 -3.622183 -2.780603 2.344832
## SODA TCELL THYAO TKFEN TOASO TRKCM TSKB
## [1,] -2.785086 -1.486513 -2.158276 -2.923705 -0.3407672 -3.423539 0.7260604
## [2,] -2.743362 -1.537597 -2.164857 -2.899013 -0.3300781 -3.433621 0.7260604
## [3,] -2.714242 -1.560285 -2.168147 -2.899013 -0.3300781 -3.443541 0.6889005
## [4,] -2.730837 -1.503631 -2.154986 -2.897346 -0.2819445 -3.423539 0.7260604
## [5,] -2.760036 -1.685346 -2.164857 -2.902292 -0.2819445 -3.404025 0.6517405
## [6,] -2.760036 -1.668295 -2.164857 -2.903940 -0.2659109 -3.404025 0.6139507
## TTKOM TUKAS TUPRS USAK VAKBN VESTL YATAS
## [1,] 1.778103 -0.7607138 -2.834480 -0.7634023 -0.1576771 -1.431407 -2.677260
## [2,] 1.778103 -0.7525807 -2.836208 -0.7876052 -0.1681164 -1.449260 -2.677260
## [3,] 1.757811 -0.7525807 -2.839660 -0.7876052 -0.1889951 -1.444796 -2.679217
## [4,] 1.778103 -0.7525807 -2.825847 -0.7634023 -0.1473454 -1.435870 -2.677260
## [5,] 1.666392 -0.7607138 -2.832756 -0.7634023 -0.1681164 -1.426944 -2.681107
## [6,] 1.686684 -0.7688469 -2.834480 -0.7876052 -0.1576771 -1.431407 -2.683063
## YKBNK YUNSA ZOREN
## [1,] 1.0379526 -1.763650 -0.09981568
## [2,] 0.9896713 -1.763650 -0.18555073
## [3,] 0.9737434 -1.763650 -0.18555073
## [4,] 1.0217759 -1.756652 -0.18555073
## [5,] 1.0379526 -1.721732 -0.14242021
## [6,] 1.0379526 -1.735729 -0.14242021
First I created the correlation matrix by using thw whole dataset.
corr_matrix <- cor(data_normalized)
ggcorrplot(corr_matrix)
However, we cannot comment much about it. It does not help much, since there are 61 variables. So, I decide to continue principal compenent analysis by using 10 preffered stocks.
Preffered_stock = Table
Preffered_normalized <- scale(Table)
corr_matrix2 <- cor(Preffered_normalized) ;corr_matrix2
## AKBNK AKSA AKSEN SASA CCOLA GARAN
## AKBNK 1.00000000 0.4945576 0.09737377 -0.26715462 0.6847650 0.90683334
## AKSA 0.49455760 1.0000000 0.63575278 0.24921114 0.3338116 0.35995332
## AKSEN 0.09737377 0.6357528 1.00000000 0.27452956 0.2354630 -0.08381838
## SASA -0.26715462 0.2492111 0.27452956 1.00000000 -0.3838070 -0.07987443
## CCOLA 0.68476501 0.3338116 0.23546301 -0.38380701 1.0000000 0.57486033
## GARAN 0.90683334 0.3599533 -0.08381838 -0.07987443 0.5748603 1.00000000
## MGROS 0.72044582 0.6255036 0.59278858 -0.30141018 0.6744950 0.50030600
## TCELL 0.40664136 0.1212738 -0.17632080 0.38985370 0.1470695 0.64586803
## TUPRS -0.22449139 -0.1178043 -0.28179930 0.66132362 -0.3419093 0.07634485
## MGROS TCELL TUPRS
## AKBNK 0.720445817 0.406641356 -0.22449139
## AKSA 0.625503630 0.121273814 -0.11780427
## AKSEN 0.592788581 -0.176320804 -0.28179930
## SASA -0.301410184 0.389853704 0.66132362
## CCOLA 0.674494967 0.147069493 -0.34190929
## GARAN 0.500305997 0.645868028 0.07634485
## MGROS 1.000000000 0.001165309 -0.54613207
## TCELL 0.001165309 1.000000000 0.57765782
## TUPRS -0.546132075 0.577657815 1.00000000
ggcorrplot(corr_matrix2)
data.pca <- princomp(corr_matrix2)
summary(data.pca)
## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 1.0512666 0.6397327 0.23752960 0.097846982 0.081711313
## Proportion of Variance 0.6948427 0.2573108 0.03547287 0.006019434 0.004197833
## Cumulative Proportion 0.6948427 0.9521535 0.98762633 0.993645761 0.997843595
## Comp.6 Comp.7 Comp.8 Comp.9
## Standard deviation 0.051265632 0.0239374997 0.0151207619 5.383804e-09
## Proportion of Variance 0.001652393 0.0003602619 0.0001437501 1.822381e-17
## Cumulative Proportion 0.999495988 0.9998562499 1.0000000000 1.000000e+00
We see that highest correlation value appers in between AKBNK and GARAN as we earlier found out.
data.pca$loadings[, 1:2]
## Comp.1 Comp.2
## AKBNK 0.3594323 0.3291475
## AKSA 0.1980731 -0.2499426
## AKSEN 0.1705985 -0.5475413
## SASA -0.3926563 -0.1955280
## CCOLA 0.3970042 0.1638051
## GARAN 0.2054261 0.4683821
## MGROS 0.4604257 -0.1034138
## TCELL -0.1618587 0.4374158
## TUPRS -0.4585743 0.2071644
fviz_screeplot(data.pca, addlabels = TRUE)
fviz_pca_var(data.pca, col.var = "black")
fviz_cos2(data.pca, choice = "var", axes = 1:2)
fviz_pca_var(data.pca, col.var = "cos2",
gradient.cols = c("black", "orange", "green"),
repel = TRUE)
You can find the related figures and the following comment on Google Trend Analysis in the report file.